Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Irada Pflüger; Tassilo Wald; Fabian Isensee; Marianne Schell; Hagen Meredig; Kai Schlamp; Denise Bernhardt; Gianluca Brugnara; Claus Peter Heußel; Juergen Debus; Wolfgang Wick; Martin Bendszus; Klaus H Maier-Hein; Philipp Vollmuth

doi:10.1093/noajnl/vdac138

Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Neurooncol Adv. 2022 Aug 23;4(1):vdac138. doi: 10.1093/noajnl/vdac138. eCollection 2022 Jan-Dec.

Authors

Irada Pflüger¹, Tassilo Wald², Fabian Isensee², Marianne Schell¹, Hagen Meredig¹, Kai Schlamp³, Denise Bernhardt⁴, Gianluca Brugnara¹, Claus Peter Heußel^{3

5}, Juergen Debus^{6

7

8

9}, Wolfgang Wick^{10

11}, Martin Bendszus¹, Klaus H Maier-Hein², Philipp Vollmuth¹

Affiliations

¹ Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
² Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
³ Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Clinic for Thoracic Diseases (Thoraxklinik), Heidelberg University Hospital, Heidelberg, Germany.
⁴ Department of Radiation Oncology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany.
⁵ Member of the Cerman Center for Lung Research (DZL), Translational Lung Research Center (TLRC), Heidelberg, Germany.
⁶ Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
⁷ Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg University Hospital, Heidelberg, Germany.
⁸ German Cancer Consotium (DKTK), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
⁹ Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
¹⁰ Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany.
¹¹ Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Abstract

Background: Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM.

Methods: A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity).

Results: The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001-0.2 cm³; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6-0.91) and 0.90 (IQR = 0.85-0.94) in the institutional as well as 0.79 (IQR = 0.67-0.82) and 0.84 (IQR = 0.76-0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63-0.92) and 0.79 (IQR = 0.63-0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76-0.94) and 0.76 (IQR = 0.68-0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92-0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72-0.91) in the external test dataset.

Conclusion: The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.

Keywords: artificial intelligence; artificial neural network; brain metastasis; magnetic resonance imaging; neuro-Oncology.